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This study aims to analyze user satisfaction with the Hospital Management Information System (SIMRS) implemented at Pelni Hospital using the SERVQUAL (Service Quality) method. The SERVQUAL method is used to measure Service Quality based on five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The research was conducted by distributing questionnaires to 31 respondents, including patients, medical staff, and administrative staff who actively use SIMRS. The results indicate that, overall, the quality of SIMRS services at Pelni Hospital is categorized as "Good" across all dimensions measured. The reliability dimension scored the highest (4.13), indicating that the system is reliable in providing services as promised. The assurance (4.06) and empathy (3.94) dimensions also received good scores, indicating that users feel safe and well-attended while using the system. However, the responsiveness (3.77) and tangibles (3.87) dimensions showed areas for improvement, such as the speed of response to user requests and improvements in the system's physical appearance, which can affect user comfort. The conclusion of this study is that SIMRS at Pelni Hospital provides adequate and satisfactory services, but improvements in the responsiveness and tangibles aspects are necessary to enhance overall user satisfaction.

The Utilization of GPS and GSM technology in operational vehicle monitoring offers convenience and efficiency in management. This research designs a GPS-based vehicle monitoring system that is integrated with vehicle maintenance management to assist companies in making decisions related to fuel efficiency, predictive maintenance scheduling, and safety aspects. By monitoring vehicle position and condition in real-time, companies can optimize travel routes, reduce travel time and reduce operational costs. The system also includes tire pressure and fuel percentage monitoring, which serve as important indicators in an accurate and sustainable vehicle maintenance strategy. This research uses quantitative methods to analyze various aspects in designing a GPS-based monitoring system that is integrated with operational vehicle maintenance management. With the resulting data, managers are able to identify trends that support predictive maintenance as well as preventive steps to reduce the risk of sudden damage. The research results show that the integration of GPS and GSM provides significant benefits in improving performance and reliability for vehicles, especially in the distribution and logistics sectors. The implementation of this system is expected to support the company in maintaining optimal vehicle performance and contribute to operational sustainability.

The Employee Attendance Information System is a crucial component in human resource management at SMP Negeri 15 Jakarta, a vocational high school institution. This study aims to analyze the quality of the attendance information system using the McCall Method, which provides a comprehensive evaluation framework. The analysis measures indicators of correctness, usability, reliability, and efficiency. The evaluation results show that the attendance system achieved average scores ranging from 0.74 to 0.76, with a total score of 298 out of an ideal score of 140, placing it in the good category. While the system functions well and positively contributes to management, there remains significant room for further development. This study identifies factors influencing system quality, which can serve as a basis for future improvements. The recommendations derived are expected to support the development of a more effective and efficient attendance information system, enhancing employee discipline and productivity, and contributing to improved educational quality for students.

Installation take care road is one of facility House the most sick used by clients or patient and also is access First for every patient For get the care needed , so that very important for House Sick For ensure that every means infrastructure , and also the provision of quality human resources For ensure quality service in maintain satisfaction patient . Research This done For know influence partial and simultaneous from performance nurses , facilities , and quality service to satisfaction patient . Research This is study quantitative descriptive , for describe influence between variable X to Y, which is done against 101 patients take care Road in the House Bandung Adventist Hospital , data inside study This collected use questionnaires that have been tested for validity and reliability, which are then processed use SPSS application , and presented in form table . Found that performance nurse No own influence significant to satisfaction patient with mark significant 0.249, while facilities and quality service own influence significant with mark respectively 0.006 and 0.000. In simultaneous performance nurses , facilities and quality service own influence significant with value 0.000 with 67.9% of satisfaction patient explained by the variables discussed in study this . Satisfaction patient influenced by performance nurses , facilities , and quality service in a way together , because That important for House Sick For Keep going maintain and improve it .

This study aims to explore the influence of work motivation, leadership style, and compensation on employee performance at Rumah Sakit Advent Bandung. With a total population of 720 employees, the study uses a simple random sampling technique to select a sample of 88 respondents, deemed sufficiently representative. Data collection is conducted through a Likert-scale questionnaire designed to capture respondents' perceptions of work motivation, leadership style, and compensation variables. To ensure data quality, the instrument's validity is tested through factor analysis, and its reliability is measured using Cronbach’s Alpha to guarantee consistency in results. Hypothesis testing is carried out using classical assumption tests and multiple linear regression analysis via SPSS version 27.The results reveal that work motivation and compensation have a positive and significant impact on employee performance, indicating that higher motivation and compensation are associated with improved employee performance. In contrast, leadership style does not show a significant effect on employee performance, possibly due to varying perceptions of the role of leadership within this organizational context. Collectively, work motivation, leadership style, and compensation variables have a significant effect on employee performance. The obtained R Square value of 0.309 indicates that 30.9% of the variation in employee performance can be explained by these three variables, while the remaining 69.1% is influenced by other factors outside this model, such as work environment, organizational culture, or other individual factors. These findings provide valuable insights for management in designing strategies to enhance employee performance.

This study aims to analyze user satisfaction with the inventory management application for Regional-Owned Assets (BMD) at SMK Negeri 17 Jakarta using the SERVQUAL method. The application was implemented to enhance asset management efficiency, but challenges such as data inaccuracies and limited user skills hinder its effectiveness. The SERVQUAL method evaluates five service quality dimensions: tangibles, reliability, responsiveness, assurance, and empathy, by measuring the gap between user expectations and perceptions. The findings reveal that all service quality dimensions have negative GAP values, particularly in reliability and tangibles, indicating that the application has not fully met user expectations. Therefore, improvements in system reliability, application design, and technical training are essential to optimize the use of the application for effective asset management in the school.

This study uses a descriptive method with a quantitative approach to analyze the numeracy literacy ability of grade XII IPS-6 students at SMAN 1 Mojosari. The sample consisted of 34 students who were selected using the purposive sampling technique. This research began with the identification of the problem of low numeracy literacy in mathematics. A numeracy test in the form of 8 story questions related to statistics is given as a pretest to measure students' abilities. The data was analyzed using statistical tests with the help of SPSS to test the validity, reliability, and results of the pretest. The results showed that 73.53% of students were in the advanced category, and 26.47% of students were in the capable category. Students are able to do histogram-shaped problems well, but have difficulty in presenting data in polygon form. These findings support previous research that states that students often have difficulty processing information from graphs and tables. Other factors that affect students' ability to flip

The background of this research focuses on the development of the beauty salon industry which is getting heavier. Beauty salons offer a variety of services aimed at improving the appearance. Beauty salons must understand the factors that affect customer satisfaction. This study aims to analyze the influence of facilities and service quality on the level of customer satisfaction in beauty salons in Talawi District, Sawahlunto City. This research is a type of causal associative research using a quantitative approach. The data collection technique used in this study is to distribute a questionnaire through a g-form that has been tested for validity and reliability to 82 respondents as a research sample. . The data collected includes personal information of salon respondents regarding the facilities provided and the quality of services received. The results of the study show that facilities and service quality significantly affect customer satisfaction. The results of the hypothesis test showed that the three hypotheses proposed were accepted. The Adjusted R Square value of 0.745 shows that 74.5% of the variation in customer satisfaction can be explained by facilities and service quality, while 25.5% can be explained by other variables outside this study. Therefore, it is important for beauty salons to improve facilities and service quality to increase customer satisfaction in order to meet customer expectations and increase their loyalty to the salon.

This study aims to analyze the quality of internet service at CV. Asri Jaya Mandiri using the Quality of Service (QoS) and SERVQUAL methods. QoS measures network performance based on parameters such as throughput, delay, jitter, and packet loss, while SERVQUAL evaluates employees' perceptions and expectations across five dimensions: tangible, reliability, responsiveness, assurance, and empathy. The analysis reveals significant gaps, particularly in reliability and empathy, with unstable connections and inadequate access speed being the primary issues. To improve Service Quality, the study recommends infrastructure upgrades, increased bandwidth, and enhanced communication within the IT team. Implementing these measures is expected to ensure smooth operations, improve employee satisfaction, and enhance the company’s competitiveness.

The Fast Fourier Transform (FFT) method for solving the 1-D heat diffusion equation offers an efficient approach for resolving partial differential equations (PDEs) with various time steps . FFT is used to transform the 1-D heat diffusion equation into the frequency domain and back to the time domain through inverse FFT. Using mathematical modeling with initial and Dirichlet boundary conditions, the numerical solutions produced by FFT are compared with analytical solutions. The accuracy of the method is validated using MAE and MSE calculated in Matlab. At several time intervals , the obtained MAE and MSE values indicate a good agreement between the numerical and analytical solutions, with very small errors. Numerical stability analysis confirms the reliability of the FFT method across various  The variation in time step  has a significant impact on the accuracy and stability of the solution. Smaller time steps improve accuracy and stability but require longer computation times. The optimal time step selected in this study is  Increasing the number of discretization points  also enhances accuracy but implies an increase in computational load and memory usage. The FFT method demonstrates good numerical consistency with increasing  

The finding of phenomena related to the physical and non-physical work environment on employee performance at the Grand Lagoi Hotel Bintan is the reason for conducting this research. This research is aimed at investigating how the physical and non-physical work environment is influenced by employee performance. By using a survey methodology based on a quantitative causal associative framework, this research seeks to explain the causal dynamics that occur. Then, the population used was employees at the Grand Lagoi Hotel Bintan. A total of 77 respondents participated in this study, selected through saturated sampling techniques to ensure comprehensive representation. To assess the validity and reliability of the questionnaire—the main instrument for data collection using a Likert scale. For data analysis, we used the SPSS 20.00 application, ensuring robust and accurate processing of the findings. The resulting findings indicate that the results of the Physical and Non-Physical Work Environment have a significant influence on Employee Performance at Grand Lagoi Hotel Bintan.

The discovery of phenomena related to visitor experiences regarding Generation Z's interest in returning to the Linggai Park tourist attraction is the main reasons for conducting this research. The goals of the research is to analyze how interest in returning to visit is influenced by the visitor's experience. A survey method combined with a causal associative quantitative methodology is used in this kind of research to investigate cause and effect. The population is generation Z visitors to the Linggai Park tourist attraction. 337 respondents in all were selected for this study utilizing the purposive sampling approach, a non-probability sampling methodology.. The questionnaire employed as a data collection instrument is evaluated for validity and reliability using the Likert scale. SPSS 20.00 and Smart PLS 3.0 were utilized to handle research data. This research indicate that the outcomes of visitor experiences have a significant influence on Generation Z's interest in returning to the Linggai Park tourist attraction.

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.

In an increasingly interconnected world, computer networks play a crucial role in business operations. This research focuses on the analysis of the client-server network system at PT. XYZ, which shares a building with several other companies. The objective is to evaluate the current network infrastructure, identify potential issues, and propose improvements to enhance performance and security. A qualitative approach was employed, utilizing case study methods, interviews, and observations to gather in-depth data on the network's configuration, IP address management, and security measures. The findings indicate that while the existing network functions adequately, there are significant risks associated with user access rights and the need for regular maintenance. The implications of this study suggest that implementing stricter security policies and upgrading bandwidth could mitigate risks and improve overall network reliability, thereby supporting business productivity.

This study aims to explore the application of blockchain in enhancing server security to mitigate ransomware and malware attacks in critical infrastructures such as healthcare, finance, and government sectors. Using a systematic literature review (SLR) approach, the research collects articles from four major databases (IEEE Xplore, Scopus, ScienceDirect, and SpringerLink) published between 2020 and 2024. The search focuses on keywords related to blockchain, server security, ransomware, malware, and attack mitigation. The results indicate that blockchain enhances data integrity, transaction security, and strengthens access control to protect sensitive data. Moreover, integrating blockchain with intrusion detection systems (IDS) and using smart contracts accelerates threat detection and response, allowing for automatic blocking and data recovery from attacks. This technology reduces reliance on manual intervention and increases operational efficiency. However, the main challenges in its implementation include high implementation costs, scalability, and technical complexity. Nevertheless, blockchain offers significant solutions for mitigating ransomware and malware attacks while enhancing the reliability and efficiency of systems. In conclusion, blockchain provides an effective solution for server security and cyber threat mitigation, although challenges related to cost and scalability need to be addressed. Further research is required to develop more efficient blockchain protocols and integrate them with other technologies to enhance threat detection and response speed.

This research is motivated by several problems. The aim of this research is to analyze the influence of product quality and service quality on consumer loyalty at Mamayu Resto, Solok City. The type of research used in this research is descriptive with quantitative data. The type of data used in this research is primary data. The population of this research is visitors who come to Mamayu Resto, Solok City. The research sample consisted of 93 consumers with a sampling technique using non-probability sampling. The data collection technique was carried out using a questionnaire with a Likert scale which had been tested for validity and reliability. Data from this research were processed using SPSS 25.00. The results of this research show that the product quality variable shows the sufficient category, with a percentage of 46.24%. The service quality variable shows the sufficient category, with a percentage of 59.14%.  The consumer loyalty variable shows the sufficient category, with a percentage of 51.61%. The variables product quality and service quality on consumer loyalty have an influence of 13.8% and 86.2% are influenced by several other factors.

The performance of MSMEs can be measured by achieving good performance during a certain period. Good MSME performance can contribute to improving the country's economy. The purpose of this study was to determine the impact of management knowledge and innovation on MSME performance in MSMEs in Aceh. The type of research used is quantitative descriptive research. The population in this study were MSME actors in Aceh. The sample used was non-probability sampling using the Slovin formula with a total sample of 228 respondents. The methods used in this study were validity test, reliability test, normality test, multicollinearity test, heteroscedasticity test, multiple linear regression test, partial test (t), Simultaneous test (F), and determination test (R). Data were processed using SPSS software version 25. The results of this study indicate that knowledge management has a significant effect on MSME performance in MSME actors in Aceh. Innovation has a positive and significant effect on MSME performance in MSME actors in Aceh. knowledge management and innovation simultaneously have a positive and significant effect on the performance of MSMEs in Aceh.

The global clean water crisis is exacerbated by significant losses in water distribution networks (WDNs), resulting in inefficient use of both water and energy resources. Traditional methods of leak detection and pressure management often fail to address these inefficiencies, leading to substantial water wastage and high operational costs. This research aims to design a sustainable, smart water distribution system using advanced technologies such as Machine Learning (ML) for leak detection and automated pressure control. The system employs real-time monitoring through IoT sensors, which continuously gather data on water pressure, flow rates, and other critical parameters. This data is analyzed using various ML algorithms, including supervised and unsupervised learning models, to detect anomalies indicative of leaks. Additionally, the system integrates automated pressure control mechanisms that dynamically adjust pressure to prevent over-pressurization, reducing both water loss and energy consumption. By combining leak detection and pressure control, the proposed system offers a more efficient, sustainable solution to water resource management compared to traditional methods. The expected outcomes include a significant reduction in water loss, enhanced energy efficiency, and improved water service quality. However, the implementation of such a system in rural or small-town infrastructure faces challenges, including sensor maintenance, algorithm reliability, and regulatory issues. A cost-benefit analysis suggests that while the initial investment in smart technologies may be high, the long-term savings in water and energy costs outweigh these costs. This study underscores the potential of ML-based systems in enhancing water conservation, operational efficiency, and sustainability in water management.

The increasing demand for sustainable and reliable energy solutions in remote and off-grid communities has led to the exploration of hybrid bio-solar renewable energy systems. This paper examines the implementation of a hybrid bio-solar system with energy storage to improve microgrid stability and support local sustainability. By combining biomass and solar power with energy storage, the system provides a reliable, clean, and cost-effective energy supply, addressing intermittency and enhancing grid resilience. The research covers system design, power flow simulation, voltage and frequency stability, and both economic and environmental analyses to assess system effectiveness. The results show that the hybrid bio-solar system significantly improves energy reliability, reduces carbon emissions, and offers a more stable energy supply compared to diesel or grid-based systems. The system's scale and configuration are crucial in optimizing cost-effectiveness, with larger systems yielding better long-term benefits. Local community involvement is key to managing and maintaining the system, ensuring its sustainability. Compared to other renewable energy hybrids like wind-solar or solar-diesel, the bio-solar system offers superior reliability and cost-efficiency, especially where biomass is abundant. The paper concludes with recommendations for scaling these systems and emphasizes the importance of supportive policies for accelerating sustainable energy transitions.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.